Uncovering Respiratory Effects of Short-term PM2.5 Exposure through Global Nocturnal Cough Monitoring

Air pollution remains one of the leading environmental determinants of health, yet the short-term effects of fine particulate matter (PM₂.₅) on early respiratory symptoms are poorly characterized owing to the limited availability of high-resolution, population-scale data. Enabled by global mobile sensing through a widely used application (Sleep Cycle) and AI algorithms that automatically detect coughs from nocturnal audio recordings, we derived nighttime cough frequency across 34 cities in 12 countries on five continents over 500 consecutive days, conducting the first multi-city assessment linking daily PM₂.₅ exposure to nocturnal respiratory symptoms. Single-city generalized additive models, multi-city panel regressions adjusting for meteorological variables and influenza rates, and quasi-experimental analyses consistently showed that same-day PM₂.₅ exposure was associated with higher cough frequency (relative risk = 1.004, 95% CI 1.001–1.008 per 10 μg/m³), with amplified effects during influenza peaks. The exposure–response curve was monotonic but nonlinear, with measurable risks evident even below the World Health Organization 24-hour guideline (15 μg/m³) and sharply increasing risks above 50 μg/m³. These findings provide real-world, population-level, multi-city evidence of acute respiratory effects from short-term PM₂.₅ exposure, reveal the absence of a safe threshold, and underscore the urgency of stringent air-quality interventions to protect respiratory health. This study also demonstrates how large-scale, AI-based mobile sensing can transform real-time environmental epidemiology by enabling population-level monitoring of early respiratory symptoms worldwide.